Model Code¶
In [1]:
### Import the libraries
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
import os
import time
from skimage.feature import hog
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
print("done")
In [2]:
### Build Images Variables
#Vehicle images
basedir = 'dataset/vehicles/vehicles/'
image_types = os.listdir(basedir)
cars = []
for imtype in image_types:
cars.extend(glob.glob(basedir+imtype+'/*'))
print('Number of Vehicle Images found:', len(cars))
with open("cars.txt", 'w') as f:
for fn in cars:
f.write(fn+'\n')
#Non-vehicle images
basedir = 'dataset/non-vehicles/non-vehicles/'
image_types = os.listdir(basedir)
notcars = []
for imtype in image_types:
notcars.extend(glob.glob(basedir+imtype+'/*'))
print('Number of Non-Vehicle Images found:', len(notcars))
with open("notcars.txt", 'w') as f:
for fn in notcars:
f.write(fn+'\n')
print("done")
In [3]:
### Define Functions
#def convert_color(img, conv='RGB2YCrCb'):
# if conv == 'RGB2YCrCb':
# return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
# if conv == 'BGR2YCrCb':
# return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
# if conv == 'RGB2LUV':
# return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
#
#
# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
def bin_spatial(img, size=(32, 32)):
color1 = cv2.resize(img[:,:,0], size).ravel()
color2 = cv2.resize(img[:,:,1], size).ravel()
color3 = cv2.resize(img[:,:,2], size).ravel()
return np.hstack((color1, color2, color3))
## Define a function to compute binned color features
#def bin_spatial(img, size=(32, 32)):
# # Use cv2.resize().ravel() to create the feature vector
# features = cv2.resize(img, size).ravel()
# # Return the feature vector
# return features
def color_hist(img, nbins=32): #bins_range=(0, 256)
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins)
channel2_hist = np.histogram(img[:,:,1], bins=nbins)
channel3_hist = np.histogram(img[:,:,2], bins=nbins)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
## Define a function to compute color histogram features
## NEED TO CHANGE bins_range if reading .png files with mpimg!
#def color_hist(img, nbins=32, bins_range=(0, 256)):
# # Compute the histogram of the color channels separately
# channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
# channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
# channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
# # Concatenate the histograms into a single feature vector
# hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# # Return the individual histograms, bin_centers and feature vector
# return hist_features
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
file_features = []
# Read in each one by one
image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
file_features.append(hog_features)
features.append(np.concatenate(file_features))
# Return list of feature vectors
return features
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True, vis=False):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
if vis == True:
hog_features, hog_image = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=True, feature_vec=True)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#9) Return concatenated array of features
if vis == True:
return np.concatenate(img_features), hog_image
else:
return np.concatenate(img_features)
# Define a function you will pass an image
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=9,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
#6) Predict using your classifier
prediction = clf.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
#plotting multiple images - based on the tutorial
def visualize(fig, rows, cols, imgs, titles):
for i, img in enumerate(imgs):
plt.subplot(rows, cols, i+1)
plt.title(i+1)
img_dims = len(img.shape)
if img_dims < 3:
plt.imshow(img, cmap='hot')
plt.title(titles[i])
#plt.imsave('output_images/'+titles[i]+'_test.png',img*255)
#plt.imsave('output_images/'+titles[i]+'_test.png',cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
#plt.imsave('output_images/'+'title'+'_test.png',car_hog_image, cmap='gray')
#cv2.imwrite('/output_images/' + titles[i] + '.png',img)
else:
plt.imshow(img)
plt.title(titles[i])
#plt.imsave('output_images/'+titles[i]+'_test.png',img*255)
#plt.imsave('output_images/'+titles[i]+'_test.png',cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
#plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
#cv2.imwrite('output_images/'+titles[i]+'_test.png',img*255)
#cv2.imwrite('/output_images/' + titles[i] + '.png',img)
print("done")
In [4]:
# Use HOG on test images
%matplotlib inline
#random indices selection
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
#read images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
#tweak these parameters and see how the results change.
color_space = 'RGB' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
#orient = 9 # HOG orientations
orient = 6 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 0 # Can be 0, 1, 2, or "ALL"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 16 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
car_features, car_hog_image = single_img_features(car_image, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat, vis=True)
notcar_features, notcar_hog_image = single_img_features(notcar_image, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat, vis=True)
images = [car_image, car_hog_image, notcar_image, notcar_hog_image]
titles = ['car image', 'car HOG image', 'notcar_image', 'notcar HOG image']
fig = plt.figure(figsize=(12,3))
visualize(fig, 1, 4, images, titles)
print("done")
for i, img in enumerate(images):
plt.imsave('output_images/'+titles[i]+'_test.png',images[i]*255)
In [5]:
# Train the Model - only on a subset of samples
#tweak these parameters and see how the results change.
#color_space = 'RGB' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
#orient = 6 # HOG orientations
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
#hog_channel = 0 # Can be 0, 1, 2, or "ALL"
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
#spatial_size = (16, 16) # Spatial binning dimensions
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 16 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
#y_start_stop = [None, None] # Min and max in y to search in slide_window()
#y_start_stop = [400,656]
#y_start_stop = [400,724]
#y_start_stop = [654,656]
t=time.time()
n_samples = 1000
random_idxs = np.random.randint(0,len(cars), n_samples)
test_cars = cars
test_notcars = notcars
#test_cars = np.array(cars)[random_idxs]
#test_notcars = np.array(notcars)[random_idxs]
car_features = extract_features(test_cars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(test_notcars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
print(time.time()-t, 'Seconds to compute features...')
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
#X_train, X_test, y_train, y_test = train_test_split(
# scaled_X, y, test_size=0.2, random_state=rand_state)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.1, random_state=rand_state)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
In [6]:
# Perform a test on images to see bounding boxes
#%matplotlib inline
searchpath = 'test_images/*'
example_images = glob.glob(searchpath)
#searchpath = 'test_images/'
#example_images = glob.glob(searchpath+'/*')
images = []
titles = []
#y_start_stop = [None, None] # Min and max in y to search in slide_window()
y_start_stop = [400,656]
overlap = 0.5
#xy_window = 64
xy_window = 96
#xy_window = 128
for img_src in example_images:
t1 = time.time()
img = mpimg.imread(img_src)
draw_img = np.copy(img)
img = img.astype(np.float32)/255
print(np.min(img), np.max(img))
windows = slide_window(img, x_start_stop=[None, None], y_start_stop=y_start_stop,
xy_window=(xy_window, xy_window), xy_overlap=(overlap, overlap))
hot_windows = search_windows(img, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
window_img = draw_boxes(draw_img, hot_windows, color=(0, 0, 255), thick=6)
images.append(window_img)
titles.append('')
print(time.time()-t1, 'seconds to process one image searching', len(windows), 'windows')
fig = plt.figure(figsize=(12,18), dpi=300)
visualize(fig, 5, 2, images, titles)
In [7]:
#plt.imshow(window_img)
for i, img in enumerate(images):
plt.imsave('test_images_results/test_bboxes'+str(i+1)+'.png',images[i])
In [8]:
# function for converting color spaces
def convert_color(img, conv='RGB2YCrCb'):
if conv == 'RGB2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
if conv == 'BGR2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
if conv == 'RGB2LUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
In [9]:
# Create a heatmap on test images
out_images = []
out_maps = []
out_titles = []
out_boxes = []
ystart = 400
ystop = 656
scale = 1.5
#scale = 1
for img_src in example_images:
img_boxes = []
t = time.time()
count = 0
img = mpimg.imread(img_src)
draw_img = np.copy(img)
heatmap = np.zeros_like(img[:,:,0])
img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
nxblocks = (ch1.shape[1] // pix_per_cell) - 1
nyblocks = (ch1.shape[0] // pix_per_cell) - 1
nfeat_per_block = orient*cell_per_block**2
window = 64
nblocks_per_window = (window // pix_per_cell) - 1
cells_per_step = 2
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
count += 1
ypos = yb*cells_per_step
xpos = xb*cells_per_step
#extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
#extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64, 64))
#get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
#scale features and make predictions
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)))
#test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
cv2.rectangle(draw_img, (xbox_left, ytop_draw+ystart), (xbox_left+win_draw, ytop_draw+win_draw+ystart),(0,0,255))
img_boxes.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw, ytop_draw+win_draw+ystart)))
heatmap[ytop_draw+ystart:ytop_draw+win_draw+ystart, xbox_left:xbox_left+win_draw] += 1
print(time.time()-t, 'seconds to run, total windows = ', count)
out_images.append(draw_img)
out_titles.append(img_src[-9:-4] + '_org_with_boxes.png')
out_titles.append(img_src[-9:-4] + '_just_heatmap.png')
out_images.append(heatmap)
out_maps.append(heatmap)
out_boxes.append(heatmap)
fig = plt.figure(figsize = (12,24))
visualize(fig, 8, 2, out_images, out_titles)
In [10]:
#plt.imshow(window_img)
for i, img in enumerate(out_images):
plt.imsave('test_images_results/'+out_titles[i],out_images[i])
In [11]:
### Detect Vehicles
#dist_pickle = pickle.load( open("svc_pickle.p", "rb" ) )
#svc = dist_pickle["svc"]
#X_scaler = dist_pickle["scaler"]
#orient = dist_pickle["orient"]
#pix_per_cell = dist_pickle["pix_per_cell"]
#cell_per_block = dist_pickle["cell_per_block"]
#spatial_size = dist_pickle["spatial_size"]
#hist_bins = dist_pickle["hist_bins"]
#img = mpimg.imread('test_image.jpg')
# Define a single function that can extract features using hog sub-sampling and make predictions
#def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
def find_cars(img, scale):
draw_img = np.copy(img)
heatmap = np.zeros_like(img[:,:,0])
img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
#nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
nblocks_per_window = (window // pix_per_cell) - 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
#test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
#cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
cv2.rectangle(draw_img, (xbox_left, ytop_draw+ystart), (xbox_left+win_draw, ytop_draw+win_draw+ystart),(0,0,255))
img_boxes.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw, ytop_draw+win_draw+ystart)))
heatmap[ytop_draw+ystart:ytop_draw+win_draw+ystart, xbox_left:xbox_left+win_draw] += 1
return draw_img, heatmap
#ystart = 400
#ystop = 656
#scale = 1.5
#out_img = find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
#plt.imshow(out_img)
In [12]:
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
In [13]:
out_images = []
out_maps = []
ystart = 400
ystop = 656
scale = 1.5
for img_src in example_images:
img = mpimg.imread(img_src)
out_img, heat_map = find_cars(img, scale)
threshold = 1
heat_map = apply_threshold(heat_map, threshold)
labels = label(heat_map)
#draw bounding boxes on the image
draw_img = draw_labeled_bboxes(np.copy(img), labels)
out_images.append(draw_img)
out_images.append(heat_map)
# cv2.imwrite('/test_images_results/test1_bbox.jpg',draw_img)
# cv2.imwrite('/test1_heatmap.jpg',heat_map)
fig = plt.figure(figsize = (12,24))
visualize(fig, 8, 2, out_images, out_titles)
In [14]:
def process_image(img):
out_img, heat_map = find_cars(img, scale)
threshold = 1
heat_map = apply_threshold(heat_map, threshold)
labels = label(heat_map)
draw_img = draw_labeled_bboxes(np.copy(img), labels)
return draw_img
In [15]:
test_output = 'test.mp4'
#clip = VideoFileClip("project_video.mp4")
clip = VideoFileClip("test_video.mp4")
test_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!
%time test_clip.write_videofile(test_output, audio=False)
#test_clip.write_videofile(test_output, audio=False)
In [16]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(test_output))
Out[16]:
In [17]:
final_output = 'final.mp4'
clip = VideoFileClip("project_video.mp4")
final_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!
%time final_clip.write_videofile(final_output, audio=False)
#test_clip.write_videofile(test_output, audio=False)
In [18]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(final_output))
Out[18]:
In [ ]: